dric2018/animals_classification

animals_classification

Description

(to add)

Click to see the full challenge info

Experiments pipeline

• Hardware stack

  • RAM : 12 GB
  • Accelerator type : Nvidia GPU Geforce GTX 1060 Max-Q
  • VRAM : 6.1 GB
  • num workers : 4 (CPU count)

• Software stack

  • Language : Python (version 3.8.6)
  • DL library :
    • Pytorch (version 1.8.1) + Pytorch Lightning (1.2.0)

The scoring metric is Accuracy

\begin{equation}
  Accuracy = \frac{ number of correct predictions }{ total number of samples }
\end{equation}

(number of correct samples / number total samples) .

Exp 1 : resnets

resnet18 (5-folds)

• score : 0.0

Usage

• Training
• Inference

Results

• What worked:
• What didn't worked:

Acknowledgements

• The code is based on learning from the shared notebooks on internet
• Some of the snippets code copied from anywhere will liked to their source (original implementation)